Script-Based Story Matching for Cyberbullying Prevention

While the Internet and social media help keep today’s youth better connected to their friends, family, and community, the same media are also the form of expression for an array of harmful social behaviors, such as cyberbullying and cyber-harassment. In this paper we present work in progress to develop intelligent interfaces to social media that use commonsense knowledge bases and automated narrative analyses of text communications between users to trigger selective interventions and prevent negative outcomes. While other approaches seek merely to classify the overall topic of the text, we try to match stories to finer-grained “scripts” that represent stereotypical events and actions. For example, many bullying stories can be matched to a “revenge” script that describes trying to harm someone who has harmed you. These tools have been implemented in an initial prototype system and tested on a database of real stories of cyberbullying collected on MTV’s “A Thin Line” Web site

Feelbook: A social media app for teens designed to foster positive online behavior and prevent cyberbullying

This project presents a prototype for a stand-alone social media application designed for teenage users in order to prevent and mitigate mean and cruel online behavior. The purpose of the app is to create a nurturing environment where teenagers use a variety of features designed to help raise self-awareness of their own online behavior, seek support when needed, and learn to control and, when possible, correct aggressive behavior. The prototype is framed by four design principles: design for reflection, design for empathy, design for empowerment, and design for the whole. We conclude by outlining the next steps in our project to develop an application that helps to improve the online experiences of young people. This work has implications for the CHI community because it applies software solutions to tackle a critical social problem that can affect the health and well being of young people

Approaches to automated detection of cyberbullying:A Survey

Research into cyberbullying detection has increased in recent years, due in part to the proliferation of cyberbullying across social media and its detrimental effect on young people. A growing body of work is emerging on automated approaches to cyberbullying detection. These approaches utilise machine learning and natural language processing techniques to identify the characteristics of a cyberbullying exchange and automatically detect cyberbullying by matching textual data to the identified traits. In this paper, we present a systematic review of published research (as identified via Scopus, ACM and IEEE Xplore bibliographic databases) on cyberbullying detection approaches. On the basis of our extensive literature review, we categorise existing approaches into 4 main classes, namely; supervised learning, lexicon based, rule based and mixed-initiative approaches. Supervised learning-based approaches typically use classifiers such as SVM and Naïve Bayes to develop predictive models for cyberbullying detection. Lexicon based systems utilise word lists and use the presence of words within the lists to detect cyberbullying. Rules-based approaches match text to predefined rules to identify bullying and mixed-initiatives approaches combine human-based reasoning with one or more of the aforementioned approaches. We found lack of quality representative labelled datasets and non-holistic consideration of cyberbullying by researchers when developing detection systems are two key challenges facing cyberbullying detection research. This paper essentially maps out the state-of-the-art in cyberbullying detection research and serves as a resource for researchers to determine where to best direct their future research efforts in this field

Using the Control Balance Theory to Explain Social Media Deviance

Online Social Media Deviance (OSMD) is one the rise; however, research in this area traditionally has lacked a strong theoretical foundation. Following calls to reveal the theoretical underpinnings of this complex phenomenon, our study examines the causes of OSMD from several novel angles not used in the literature before, including: (1) the influence of control imbalances (CIs) on deviant behavior, (2) the role of perceived accountability and deindividuation in engendering CI, (3) and the role of IT in influencing accountability and deindividuation. Using an innovative factorial survey method that enabled us to manipulate the IT artifacts for a nuanced view, we tested our model with 507 adults and found strong support for our model. The results should thus have a strong impetus not only on future SM research but also for social media (SM) designers who can use these ideas to further develop SM networks that are safe, supportive, responsible, and constructive

Brute - Force Sentence Pattern Extortion from Harmful Messages for Cyberbullying Detection

Cyberbullying, or humiliating people using the Internet, has existed almost since the beginning ofInternet communication.The relatively recent introduction of smartphones and tablet computers has caused cyberbullying to evolve into a serious social problem. In Japan, members of a parent-teacher association (PTA)attempted to address the problem by scanning the Internet for cyber bullying entries. To help these PTA members and other interested parties confront this difficult task we propose a novel method for automatic detection of malicious Internet content. This method is based on a combinatorial approach resembling brute-force search algorithms, but applied in language classification. The method extracts sophisticated patterns from sentences and uses them in classification. The experiments performed on actual cyberbullying data reveal an advantage of our method vis-à-visprevious methods. Next, we implemented the method into an application forAndroid smartphones to automatically detect possible harmful content in messages. The method performed well in the Android environment, but still needs to be optimized for time efficiency in order to be used in practic

Detecting Online Harassment in Social Networks

Online Harassment is the process of sending messages over electronic media to cause psychological harm to a victim. In this paper, we propose a pattern-based approach to detect such messages. Since user generated texts contain noisy language, we perform a normalization step first to transform the words into their canonical forms. Additionally, we introduce a person identification module that marks phrases which relate to a person. Our results show that these preprocessing steps increase the classification performance. The pattern-based classifier uses the information provided by the preprocessing steps to detect patterns that connect a person to profane words. This technique achieves a substantial improvement compared to existing approaches. Finally, we discuss the portability of our approach to Social Networks and its possible contribution to tackle the abuse of such applications for the distribution of Online Harassment

DETECTING CYBERBULLYING IN ONLINE COMMUNITIES

Online communities are platforms enabling their users to interact over the web. In particular, they are popular among adolescents as a tool to discuss topics of mutual interest. However, offending commu-nication is a growing issue in these online environments. In its basic form, the process of sending mes-sages over electronic media to cause psychological damage to a victim is called online harassment. In a more severe form, cyberbullying is the process of sending offending messages several times to the same victim by the same offender. In this work, we propose an approach to detect cyberbullies and their victims. Identifying and aiding victims received only brief attention in existing work. We introduce a harassment graph to capture multiple message exchanges comprising cyberbullying cases. We show that our approach is able to precisely detect cyberbullies and their victims. Additionally, we propose metrics to measure the severity of online harassment and cyberbullying cases in terms of quantitative aspects. In particular, the metrics allow to identify victims of severe cyberbullying cases and might be used as an early indicator to provide fast and selective aid by administrators. We further propose use cases for our approach in online communities to tackle the problem of cyberbullying

Examining Employee Social Media Deviance: A Psychological Contract Breach Perspective

With the prevalence of social media, employees’ deviant behaviors on social media can go viral and result in unpredictable negative outcomes beyond the workplace. This paper investigates the relationship between abusive supervision and employee social media deviance from the theoretical perspective of psychological contract breach (PCB), and examine the moderating role of social media controls. Building on prior studies of abusive supervision and employee workplace deviance, this paper argues that abusive supervision plays a crucial motivational role in triggering employee social media deviance. Our results demonstrate that employees who experience abusive supervision are more likely to perceive PCB, and thus engage in social media deviance. User awareness of social media policy and informal sanctions can weaken the positive relationship between employee perceived PCB and social media deviance